Method and system for generating a transitory sentiment community

ABSTRACT

A method and system of generating a transitory sentiment community. The method comprises identifying, in accordance with a supervised trained model, within agglomerated social media content data, content associated with a subject of interest and characterized in accordance with one of a sentiment expressive usage and not a sentiment expressive usage, the subject of interest defined in accordance with at least one text character string; performing, based on an unsupervised trained model in conjunction with content associated with the sentiment expressive usage, a sentiment analysis that determines a sentiment intensity rating associated with at least a portion of the agglomerated social media content data, and generating the transitory sentiment community based at least in part on the sentiment intensity rating.

RELATED APPLICATIONS

This application claims the benefit of priority to U.S. patentapplication Ser. No. 16/216,038 filed Dec. 11, 2018, now issued as U.S.Pat. No. ______. Said U.S. patent application Ser. No. 16/216,038 ishereby incorporated in the entirety herein.

TECHNICAL FIELD

The disclosure herein relates to generating a transitory networkcommunity.

BACKGROUND

Current techniques associate social connections of a user that can beused effectively for purposes such as promoting products and services orfor making personalized interactions in a system. Such approaches maylack a system that can collect and interpret necessary informationwithout limitations based literal interpretations of the information ascollected, while respecting privacy rights of individuals.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates, in an example embodiment, a server-based system forgenerating a transitory sentiment community.

FIG. 2 illustrates, in one example embodiment, an architecture of acomputing system for generating a transitory sentiment community.

FIG. 3 illustrates, in an example embodiment, a linguistic frameworkincluding a sarcasm sentiment module.

FIG. 4 illustrates an example embodiment of pre-processing data ingenerating a transitory sentiment community.

FIG. 5 illustrates, in an example embodiment, a method of operation ingenerating a transitory sentiment community.

FIG. 6 illustrates, in an example embodiment, an architecture of aserver computing system generating a transitory sentiment community.

FIG. 7 illustrates an example embodiment of a method of operation ingenerating a transitory sentiment community.

DETAILED DESCRIPTION

Among other technical advantages and benefits, solutions provided hereinprovide a system that generates and captures a transitory sentimentcommunity at a server computing device. The transitory nature of thesentiment community as generated ebbs and flows, being created ordeleted in accordance with threshold sentiment parameters that definethe creating or deleting of the transitory sentiment community. In someembodiments as suited for an ad hoc purpose, the transitory sentimentcommunity may be based on collective sentiment or emotion inferred fromposted content using a linguistic framework, allowing for real-time,fluid monitoring of such sentiment community in accordance with itstransitory nature, while respecting individual privacy rights associatedwith content sources.

In accordance with a first example embodiment, a method of generating atransitory sentiment community. The method comprises receiving data, ina database memory associated with a server computing device, the dataextracted from a plurality of data sources, pre-processing the data, ina processor of the server computing device based on at least one of textcharacter removal and text character replacement, to providepre-processed data that includes a set of keywords used in a descriptivemanner, performing a sentiment analysis on the set of keywords based atleast in part upon a training model, the sentiment analysis identifying:(i) a conformance to at least one sentiment classification of a set ofsentiment classifications recognized by the training model, and (ii) asentiment intensity rating associated with the conformance, modifyingthe sentiment intensity rating associated with the at least onesentiment classification upon detecting a sarcasm sentiment that isabove a sarcasm sentiment likelihood threshold, and generating thetransitory sentiment community based at least in part on the at leastone sentiment classification and the modified sentiment intensityrating.

In accordance with a second example embodiment, also provided is aserver computing system for generating a transitory sentiment community.The server computing system comprises a processor and a memory storing aset of instructions. The instructions are executable in the processor toreceive data, in a database memory associated with a server computingdevice, the data extracted from a plurality of data sources, pre-processthe data, in a processor of the server computing device, based on atleast one of text character removal and text character replacement, toprovide pre-processed data that includes a set of keywords used in adescriptive manner, perform a sentiment analysis on the set of keywordsbased at least in part upon a training model, the sentiment analysisidentifying: (i) a conformance to at least one sentiment classificationof a set of sentiment classifications recognized by the training model,and (ii) a sentiment intensity rating associated with the conformance,modify the sentiment intensity rating associated with the at least onesentiment classification upon detecting a sarcasm sentiment that isabove a sarcasm sentiment likelihood threshold, and generate thetransitory sentiment community based at least in part on the at leastone sentiment classification and the modified sentiment intensityrating.

In accordance with a third example embodiment, a non-transitory memorymedium storing instructions executable in a processor of a computingdevice is provided. The instructions are executable to receive data, ina database memory associated with a server computing device, the dataextracted from a plurality of data sources, pre-process the data, in aprocessor of the server computing device, based on at least one of textcharacter removal and text character replacement, to providepre-processed data that includes a set of keywords used in a descriptivemanner, perform a sentiment analysis on the set of keywords based atleast in part upon a training model, the sentiment analysis identifying:(i) a conformance to at least one sentiment classification of a set ofsentiment classifications recognized by the training model, and (ii) asentiment intensity rating associated with the conformance, modify thesentiment intensity rating associated with the at least one sentimentclassification upon detecting a sarcasm sentiment that is above asarcasm sentiment likelihood threshold, and generate the transitorysentiment community based at least in part on the at least one sentimentclassification and the modified sentiment intensity rating.

In one aspect, provided is a method, performed in a processor of aserver computing device, of generating a transitory sentiment community.The method comprises identifying, in accordance with a supervisedtrained model, within agglomerated social media content data, contentassociated with a subject of interest and characterized in accordancewith one of a sentiment expressive usage and not a sentiment expressiveusage, the subject of interest defined in accordance with at least onetext character string; performing, based on an unsupervised trainedmodel in conjunction with content associated with the sentimentexpressive usage, a sentiment analysis that determines a sentimentintensity rating associated with at least a portion of the agglomeratedsocial media content data, and generating the transitory sentimentcommunity based at least in part on the sentiment intensity rating.

In yet another aspect, provided is a server computing system thatincludes a processor and a non-transitory memory storing instructionsexecutable in the processor. The instructions, when executed in theprocessor, cause operations comprising identifying, in accordance with asupervised trained model, within agglomerated social media content data,content associated with a subject of interest and characterized inaccordance with one of a sentiment expressive usage and not a sentimentexpressive usage, the subject of interest defined in accordance with atleast one text character string; performing, based on an unsupervisedtrained model in conjunction with content associated with the sentimentexpressive usage, a sentiment analysis that determines a sentimentintensity rating associated with at least a portion of the agglomeratedsocial media content data, and generating the transitory sentimentcommunity based at least in part on the sentiment intensity rating.

In another aspect, provided is a non-transitory memory storinginstructions. The instructions are executable in a processor and whenexecuted, cause operations comprising identifying, in accordance with asupervised trained model, within agglomerated social media content data,content associated with a subject of interest and characterized inaccordance with one of a sentiment expressive usage and not a sentimentexpressive usage, the subject of interest defined in accordance with atleast one text character string; performing, based on an unsupervisedtrained model in conjunction with content associated with the sentimentexpressive usage, a sentiment analysis that determines a sentimentintensity rating associated with at least a portion of the agglomeratedsocial media content data, and generating the transitory sentimentcommunity based at least in part on the sentiment intensity rating.

One or more embodiments described herein provide that methods,techniques, and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device.

Furthermore, one or more embodiments described herein may be implementedthrough the use of logic instructions that are executable by one or moreprocessors of a computing device, including a server computing device.These instructions may be carried on a computer-readable medium. Inparticular, machines shown with embodiments herein include processor(s)and various forms of memory for storing data and instructions. Examplesof computer-readable mediums and computer storage mediums includeportable memory storage units and flash memory (such as carried onsmartphones). A server computing device as described herein utilizesprocessors, memory, and logic instructions stored in the memory orcomputer-readable medium. Embodiments described herein may beimplemented in the form of computer processor-executable logicinstructions or programs stored on computer-readable memory mediums.

System Description

FIG. 1 illustrates, in an example embodiment, transitory sentimentcommunity logic module 105 hosted at server computing device 101, withincloud networked system 100 for software application execution withconcurrent signaling. While server computing device 101 may includetransitory sentiment community logic module 105 as depicted, it iscontemplated that, in alternate embodiments, computing devices 102 a-c,including desktop workstations, laptop computers, and mobile computingdevices 102-c in communication via network 104 with server 101, mayinclude one or more sub-modules or portions that constitute transitorysentiment community logic module 105 as embodied in computerprocessor-executable instructions stored within a non-transitorycomputer readable memory. Database 103 may be communicatively accessibleto server computing device 101 (also referred to as server 101 herein)and also to one or more of client computing devices 102 a-c(collectively referred to herein as client computing device 102) viacommunication network 104.

Client computing devices 102 a-c may be configured to include clientinterface module 106 constituted of processor-executable instructions,allowing interaction by a user at client computing devices 102 a-c withresults generated from, or in conjunction with, transitory sentimentcommunity logic module 105 at server 101. In one embodiment, clientinterface module 106 includes processor-executable instructions or asoftware application downloadable from server computing device 101 andconfigured to operate in conjunction with transitory sentiment communitylogic module 105 to allow physical user interface representations of thesentiment community at client computing device 102. It is contemplatedthat, in alternate embodiments, client interface module 106 may includeone or more sub-modules or portions that constitute transitory sentimentcommunity logic module 105 as embodied in computer processor-executableinstructions stored within a non-transitory computer readable memory,and executable in a processor of client device 102.

FIG. 2 illustrates architecture 200 of server computing device 101hosting transitory sentiment community logic module 105, in an exampleembodiment. Server computing device 101 may include processor 201,non-transitory computer readable memory 202 that includes transitorysentiment community logic module 105, display screen 203, inputmechanisms 204 such as a keyboard or software-implemented touchscreeninput functionality, and communication interface 207 for communicatingwith client devices 102 a-c via communication network 104.

Transitory sentiment community logic module 105 includes instructionsstored in memory 202 of server 101, the instructions configured to beexecutable in processor 201. Sentiment community logic module 105 maycomprise portions or sub-modules including database module 210, datapre-processing module 211, sentiment analysis module 212, sarcasmmodification module 213 and sentiment community generating module 214.

Database module 210, in an embodiment, includes instructions executablein processor 201 in association with database module 210 to receivedata, in a database memory associated with server computing device 101,the data extracted from a plurality of data or content sources.

Data pre-processing module 211, in an embodiment, includes instructionsexecutable in processor 201 to pre-process the extracted data orcontent, based on at least one of text character removal and textcharacter replacement, to provide pre-processed data that includes a setof keywords used in a descriptive manner.

Sentiment analysis module 212, in an embodiment, includes instructionsexecutable in processor 201 to perform a sentiment analysis on the setof keywords, based at least in part upon a training model, to: (i) aidentify a conformance to at least one sentiment classification of a setof sentiment classifications recognized by the training model, and (ii)derive a sentiment intensity rating associated with the conformance. Insome example embodiments, sentiment classifications or labels mayrepresent sentiments or emotions, in addition to a “sarcasm” or“sarcastic” sentiment or emotion, as any one or more of “anger”,“disgust”, “fear”, “joy”, “sadness”, “surprise”, “trust”,“anticipation”, “distrust”, “satisfied”, “dissatisfied”, “disappointed”,“delighted”. It is contemplated that descriptions, classifications,labels and representations of sentiments or emotions may not be limitedto only the latter examples, and furthermore, need not be limited to anyparticular form of, or conformance with, particular grammatical forms inaccordance with tenses, verbs, nouns, or adjectives.

Sarcasm modification module 213, in one embodiment, includesinstructions executable in processor 201 to modify the sentimentintensity rating associated with the at least one sentimentclassification of the sentiment classifications recognized by a trainingmodel upon detecting a sarcasm sentiment that is above a sarcasmsentiment likelihood threshold. The sarcasm sentiment may be associatedwith a statistical likelihood that the set of keywords pertain tosarcasm based in art on the author of the underlying content. In oneembodiment the sarcasm sentiment likelihood threshold may be expressedas a statistical confidence level threshold value, above which thresholdvalue a sarcasm sentiment is established as detected.

Sentiment community generating module 214, in one embodiment, includesinstructions executable in processor 201 to generate the transitorysentiment community based at least in part on the sentimentclassification and the modified sentiment intensity rating in accordancewith applying sarcasm sentiment module 213.

FIG. 3 illustrates, in an example embodiment, linguistic framework 301that includes author profile component 303, pre-processing component 304which includes author profile pre-processing functions, and sarcasmidentification component 305 of sarcasm sentiment module 213 formodifying a sentiment intensity rating associated with an identifiedsentiment class. The identified sentiment class may be one, or more thanone in alternate embodiments, of a set of sentiment or emotionclassifications recognized by training model 306.

Data source input component 302 provides input data to database module210 to receive data in memory 202 or a database memory 103 associatedwith server computing device 101, the data being extracted from aplurality of data sources. The data sources comprise publicly availabledata such as, but not limited to, tweet content, instant messagingcontent, product review content, service review content, social mediaposting content, and website content.

Data gathered from content sources may be stored in an OrientDB opensource NoSQL graph database in one embodiment, allowing flexibility,scalability, rapid scaling of data, providing advantages in queryoptimization, including use of lightweight edges and indexes thatimproves the speed of data retrieval. Data objects may be stored asnodes, with corresponding relationships represented as edges. Each nodehas unique properties depending on the source, but most nodes sharecommon properties including user ID, create date, text, source,sentiment, category, and emotion parameters. Edges that connect thenodes show the relationship between nodes. Some edges have their ownproperties (regular edges) while others do not (lightweight edges).

General database optimization and tuning techniques may be applied tofurther improve database performance, such as indexing techniquesapplied to ensure advantageous balance between quick database access anddisk space, with lightweight edges incorporated to improve performanceand memory usage.

Feature engineering component 307 of linguistic framework 301 providesfeatures and operations to be applied upon pre-processed, or cleaned,data of data cleaning module 304.

FIG. 4 illustrates an example embodiment 401 in exemplary detail relatedto pre-processing data in generating a transitory sentiment community,showing pre-processing operations performed on content, including textremoval and text replacement operations.

In an embodiment that implements topic extraction operations, the atopic model based on dirilecht distributions and multinomial may be usedto provide classification and general understanding of latent topicswithin the data content, such as a review of a product or service. In anembodiment, prior knowledge of topic-word distribution may be sued toprovide an initialization to the starter distributions when selectingtopic per word and to the data content item as well. This process ofinitializing the likelihood based on prior words may be incorporatedinto an online dirilecht-based technique. Creating the prior knowledgeof topic-word distribution may be done manually for each use case toprovide custom priors and allow for a guided latent Dirichlet allocation(LDA) approach which specializes in learning topics for a specificdomain. To further improve and to be able to re-incorporate the priorswe allow for constant reminder of prior information to stay within themodel through the alpha and beta parameters of the LDA model. These twoparameters in the online LDA algorithm allow the priors to maintainrelevant throughout the inference and training processes. To calculatean optimal alpha beta contribution factor of regression analysis may beused to calculate alpha and beta parameters separated by combinations ofprior knowledge and a set of coefficients.

Topic relevance ranking may be incorporated rank the words whichdescribe each latent topic. An after-training analysis of the model andcome with up relevancy and saliency scoring metrics. Adoption of thesemetrics allows for ranking of topic words, topic separation, and topicdominance. Attaching the new relevance metrics to online LDA with priorsprovide a unique and enhanced tool for topic analysis with qualitymeasures. For topic optimization, topic modeling methods generatemultinomial topic distribution in which each topic is a set of uni-gramtokens with a probability that determines how frequent each word appearsin a text. In order to have more understandable topics, uni-gram tokensmay be replaced with high quality phrases.

In one embodiment, a set of candidate labels may be extracted from thereference corpus. Then a relevancy score assigned to each candidatelabel. To score candidate labels, Kullback-Leibler divergence may beapplied to find similarity between distribution of words in a topic anddistribution of words in a label. Candidate labels may be ranked withrespect to each topic model and top ranked labels selected to label thecorresponding topic(s), a good label being denoted as having highsemantic relevance to the target topic, and low semantic relevance toother topics.

Next in regard to topic related sentiment or emotion analysis, apre-trained model 306 may be used to capture char embeddings is a newtactic in linguistic-based models. To further the model to highaccuracies, a specialized set of keywords may identified and applied togather training data for each of a set of emotions or sentiments. Usingthe list of keywords for each emotion training data may be extractedfrom content sources. The data was cleaned prior to training to ensurethe keywords were used in a descriptive manner. Additionally, trainedmodels may be incorporated to implement a full artificial intelligence(AI)-based teacher-student model. Such AI teachers may be implemented toselect training data with high degree of confidence to be used tofurther train the emotion or sentiment model. To generate meaningfulphrases as candidate labels from reference collection, chunking parsermethods may be used to extract noun phrases. Labels which contain moreimportant words (higher probability) in the topic distributions areconsidered as good labels. Multinomial distribution of topics found withonline LDA (with setting priors as guided LDA) may be then matched withthe top-ranked candidate labels which have high relevance score to eachindividual topic.

Next with regard to the particular sentiment of sarcasm, sentiment-basedmodels may fail when sarcasm is involved in the input data content, forexample, in tweets. Sarcasm detection can help in gaining a betterinsight into customer sentiments. As incorporated into sarcasmmodification module model 213 for sarcasm sentiment detection andsentiment intensity modification, features may be analyzed from tweetsand their description in order to capture the sarcasm expressed intweets, for example. Further in reading to online content postingsincluding tweets, registered authors on have the opportunity to providea personal profile or self-description. Such self-description or authorprofile data may be used to extract information about personality traitsof content authors, to enable or help in classifying sarcastic tweetsfrom non-sarcastic ones with a high degree of accuracy. In one exampleembodiment, a range of different feature-sets from tweet-text inconjunction with author's self-description may be engineer usingLinearSVM and Logistic Regression predictive models. Further,lexical-based features 307 of linguistic framework 301 may be generatedfrom review text such as n-grams, intensifiers, number of capital letterwords, binary feature for double quotes, count of part-of-speech to namea few, as such features help in capturing the nuances of the writingstyle of a content author, such as Twitter users. Sentiment-basedfeatures may also be generated from the content. Positive, negative anddifference in sentiment score, for example from different parts of thereview text, are captured as features. This feature enables detection ofa degree contrast of sentiment in a sarcasm. Topic-modeling basedfeatures may be captured from the user profile description by applyingthe topic modeling technique. Such feature is based on the presumptionthat the author's self-description can give a better idea of aninclination towards sarcasm.

In an embodiment, a slang related sentiment may be detected and appliedto modify, such as by amplifying or attenuating, the sentiment intensityrating upon detecting existence of a slang component of the set ofkeywords. To find slang sentiments, one or more slang lexicons, such asUrban Dictionary which is a web-based dictionary of slang definitions,lists of popular idioms, and SlangSD lexicon which contains slangs andtheir associated sentiments. If a slang term or component exists in thelexicon, the slang sentiment is collected, including collecting reviewscontaining the slang with high relevance score and the slang examplescontaining the slang from the slang lexicon source. In social media,slangs may be commonly used in short forms, and informal terms. Hence,slang polarity may be identified before sentiment classification ofreviews or blogs and other content. Thus, slangs or idioms may bedetected in reviews and other content, and a sentiment score assigned toeach slang term. Such results may be aggregated based on a highestranking to produce a unique sentiment score for each slang. Since slangsusage and definition may change over time and in different data sources,the data collection of slangs may be updated continuously.

After preprocessing, steps applied to detect slangs in content mayinclude a lookup table search in conjunction with the slang lexiconsources, then filtering out all non-slang words from text to findsingle-words slangs, such as by using WordNet and English wordslexicons. Then, single words found may be cross-referenced any existingdefinition in the slang lexicon source, upon which they are detected asslang, or otherwise simply a mis-spelling. Then upon slang detection,determining whether the slang appearing in the data content isconsistent with its usage pattern in the lexicon source. In other words,if a slang in a review, for example, is consistent with its definitionfrom Urban Dictionary as lexicon source. A relevancy score may beassigned to each content item containing the slang. In each content itemthat contains a slang, the slang is removed and compared to rest of thewords in a text with the slang related words. This comparison is basedon the semantic similarity between words in the content and words in theslang related words, for example, based on word2vec embeddings trainedon Google News. If a word had a high semantic similarity to the relatedwords, it may be added to a related words list. After comparing a reviewtext with the slang related words, a list of floating numbers may bedetermined to represent a semantic distance between each pair of words.To assign the relevancy score to each data content item, the maximumsemantic distance may be selected.

Methodology

FIG. 5 illustrates, in an example embodiment, method 500 of generating atransitory sentiment community, method 500 being performed by one ormore processors 201 of server device 101. In describing the example ofFIG. 5, reference is made to the examples of FIG. 1 through FIG. 4 forpurposes of illustrating suitable components or elements in performing astep or sub-step being described.

Examples of method steps described herein relate to the use of servercomputing device 101 including transitory sentiment community logicmodule 105 for implementing the techniques described. According to oneembodiment, the techniques are performed by server computing device 101in response to the processor 201 executing one or more sequences ofsoftware logic instructions that constitute transitory sentimentcommunity logic module 105. In embodiments, transitory sentimentcommunity logic module 105 may include the one or more sequences ofinstructions within sub-modules including database module 210, datapre-processing module 211, sentiment analysis module 212, sarcasmmodification module 213 and sentiment community generating module 214.Such instructions may be read into memory 202 from machine-readablemedium, such as memory storage devices. In executing the sequences ofinstructions contained in database module 210, data pre-processingmodule 211, sentiment analysis module 212, sarcasm modification module213 and sentiment community generating module 214 of transitorysentiment community logic module 105 in memory 202, processor 201performs the process steps described herein. In alternativeimplementations, at least some hard-wired circuitry may be used in placeof, or in combination with, the software logic instructions to implementexamples described herein. Thus, the examples described herein are notlimited to any particular combination of hardware circuitry and softwareinstructions.

Additionally, it is further contemplated that in alternativeembodiments, the techniques herein, or portions thereof, may bedistributed between client, or subscriber, computing devices 102 a-c andserver computing device 101. For example, computing devices 102 a-c mayperform some portion of functionality described herein with regard tovarious modules of which transitory sentiment community logic module 105is comprised, and transmit data to server 101 that, in turn, performs atleast some portion of the techniques described herein.

At step 510, processor 201 executes instructions in association withdatabase module 210 to receive data in memory 202 or a database memory103 associated with server computing device 101, the data beingextracted from a plurality of data sources.

In some embodiments, the data sources comprise publicly available dataconstituted of such as, but not limited to, tweet content, instantmessaging content, product review content, service review content,social media posting content, and website content.

At step 520, processor 201 executes instructions of data pre-processingmodule 211 to pre-process the data, based on at least one of textcharacter removal and text character replacement, to providepre-processed data that includes a set of keywords used in a descriptivemanner.

At step 530, processor 201 executes instructions of sentiment analysismodule 212 to perform a sentiment analysis on the set of keywords basedat least in part upon a training model, the sentiment analysisidentifying: (i) a conformance to at least one sentiment classificationof a set of sentiment classifications recognized by the training model,and (ii) a sentiment intensity rating associated with the conformance tothe at least one sentiment classification.

In an embodiment, the sentiment classification comprises a subset of theset of sentiment classifications as recognized by the training model.

In one variation, the method further includes identifying a plurality oftopics based on the pre-processed data and selecting at least one topicbased on a ranking of the plurality of topics.

A commonality or frequency of occurrence of some or all keywords formingthe set may predominantly pertain to the selected topic in accordancewith the ranking.

In another embodiment, the sarcasm sentiment is detected based at leastin part on a personality trait of an author of the data from which theset of keywords is sourced or is traceable, and the pre-processingfurther comprises pre-processing data of the personality trait of theauthor, to detect a sarcasm sentiment likelihood factor. The sarcasmsentiment likelihood factor may be expressed, or quantified, as astatistical confidence level, in one embodiment.

At step 540, processor 201 executes instructions of sarcasm modificationmodule 213 to modify the sentiment intensity rating associated with theat least one sentiment classification upon detecting a sarcasm sentimentparameter that is above a sarcasm sentiment likelihood threshold value.

In an embodiment, identifying the sentiment intensity rating includesdetermining a quantifiable statistical sentiment score associated withthe sentiment classification, and the modifying includes eitheramplifying and attenuating the sentiment intensity rating of thesentiment classification.

The instructions of sarcasm modification module 213 may be furtherexecutable to modify or alter the sentiment classification to one ormore alternate sentiment classifications recognized by the trainingmodel. In a further embodiment, altering the sentiment classificationalso includes replacing the sentiment classification with a generallycontrary or opposite sentiment classification when sarcasm sentiment isdetected above a certain threshold statistical confidence level. By wayof illustration and examples, where the sentiment classification relatedto a product or services review as expressed is originally interpretedas “delighted”, then a generally contrary or opposite sentimentclassification applied in generating the sentiment community in lieu of“delighted” might be “disappointed”; where the sentiment classificationas expressed is originally interpreted as “satisfied”, then a generallycontrary or opposite sentiment classification applied in generating thesentiment community in lieu of “satisfied” might be “dissatisfied”;where the sentiment classification as expressed is originallyinterpreted as “trust”, a generally contrary or opposite sentimentclassification applied in generating the sentiment community in lieu of“trust” might be “mistrust” or “distrust”.

In an embodiment, the method further comprises modifying the sentimentintensity rating by either amplifying, or attenuating, the sentimentintensity rating upon detecting existence of a slang component of theset of keywords.

At step 550, processor 201 executes instructions of sentiment communitygeneration module 214 to generate the transitory sentiment communitybased at least in part on the at least one sentiment classification andthe modified sentiment intensity rating.

In one variation, generating the transitory sentiment community may bebased on applying a sentiment intensity threshold to the modifiedsentiment intensity rating, whereupon the generating is initiated uponthe sentiment intensity rating crossing above, or below in anotherembodiment, the modified sentiment intensity threshold.

In an embodiment, the sentiment classification comprises a subset of theset of sentiment classifications, and the method further comprisesgenerating the transitory sentiment community based on a sentimentintensity threshold associated with respective ones of the subset ofsentiment classifications.

In some variations, the transient sentiment community, includingphysical user interface representations thereof, may be generated bysentiment community generating module 114 in conjunction with clientinterface module 106 of client computing device 102 at a displayinterface or a user interface of client computing device 102.

FIG. 6 illustrates, in an example embodiment, an architecture 600 of aserver computing system generating a transitory sentiment community. Indescribing the embodiments of FIG. 6, the examples of FIG. 1 throughFIG. 5 are incorporated for purposes of illustrating suitable componentsor elements for performing techniques described herein. Non-transientmemory 202, in embodiments, includes transitory sentiment communitylogic module 605 comprised of instructions executable in processor 201.In embodiments, transitory sentiment community logic module 605 includessentiment content identification module 610, sentiment analysis module611, and sentiment community generating module 612. Among other aspects,a user can define their subject of interest, apply transitory sentimentcommunity logic module 605 to identify short phrases which representthat subject of interest, then apply this as input to generate asentiment, or emotionality, intensity rating used to generate atransitory sentiment community that is defined in accordance with thesentiment, or emotionality, intensity rating.

Sentiment content identification module 610 includesprocessor-executable instructions stored in non-transient memory 202 toidentify, in accordance with a supervised trained model, withinagglomerated social media content data, content associated with asubject of interest and characterized in accordance with either one of asentiment expressive usage or not a sentiment expressive usage, thesubject of interest defined in accordance with at least one textcharacter string.

Sentiment analysis module 611 includes processor-executable instructionsstored in non-transient memory 202 to perform, based on an unsupervisedtrained model in conjunction with content identified as associated withthe sentiment expressive usage, a sentiment analysis that determines asentiment intensity rating associated with at least a portion of theagglomerated social media content data.

Sentiment community generating module 612 includes processor-executableinstructions stored in non-transient memory 202 to generate a transitorysentiment community that is defined, at least partly, based on thesentiment intensity rating.

Training the neural network models to enable deployment of aspectstransitory sentiment community logic module 605 as described herein canutilize a 2-phase classification process to learn key content associatedwith contexts defined in accordance with a subject of interest, whichhave emotional intensity, or expressiveness, and then extrapolate andbuild the relationship between phrases and key contexts, based onemotional intensity rating or level.

In some embodiments, the neural network can be such as, but notnecessarily limited to, a convolution neural network and a recurrentneural network. In training models described herein, embodiments includea supervised or semi-supervised learning using emotional data words andperform a check to ensure the word is being used to express emotion orsentiment within the text or sentence, as opposed to merely descriptiveuse without emotional expressiveness being conveyed within a text phraseor sentence. The neural network model can be trained in accordance witha technique that utilizes predetermined phrases, or priors, which definethe desired meaning for a given set of topics as input to anunsupervised deep learning stack that builds the relationship betweentopics of interest and similar topics found in agglomerated social mediacontent. The training model builds a list of similar phrases withcontext in this manner, classifying text content as either expressiveexperiences or emotional contexts, or otherwise classifying text contentas not emotional- or sentiment-expressive.

In the particular embodiment of a convolution neural network model, theconvolution operation typically embodies two parts of inputs: (i) inputfeature map data, and (ii) a weight (also referred to as output filter,or kernel). Given the input channel data with W(Width)×H(Height)×IC datacube and RxSxICa filter, the output of direct convolution may beformulated as:

$y_{w,h} = {\underset{r = 0}{\sum\limits^{R - 1}}{\underset{s = 0}{\sum\limits^{S - 1}}{\underset{c = 0}{\sum\limits^{C - 1}}{\chi_{{({w + r})},{({h + s})},c}*w_{r,s,c}}}}}$

where:

X=input data/input feature/input feature map

w=width of the input or output data

h=height of the input or output data

R=weight size (width)

S=weight size (height)

C=number of input channel

Y=output data/output feature/output feature map

W=filter/kernel/weight

For each input channel, the filter, or weight, are convoluted with dataand generates output data. The same location of data of all the inputchannels are summed together and generate an output data channel.

A weight is applied to detect a particular defect feature or type basedon an input data stream of patient medical condition parameters.

Each output channel of the convolution model is represented by an outputfilter or weight used to detect one particular feature or pattern of theinput feature data stream. Convolution networks may be constituted ofmany output filters or weights, arranged in matrix configurations, foreach layer of the convolution model corresponding to respective featuresor patterns in the input data stream.

In embodiments, the training model is implemented in accordance with aneural network configured with a set of input layers, an output layer,and one or more intermediate layers connecting the input and outputlayers. In embodiments, the input layers are associated with inputfeatures that relate to content data agglomerated from various anddisparate sources of social media content.

In embodiments, either of the supervised and the unsupervised trainedmodel is trained, in accordance with a neural network machine learningmodel comprising a set of input layers interconnected via a set ofintermediate layers to an output layer, the machine learning neuralnetwork model being instantiated in the processor based at least in partupon processor-executable instructions being accessed by the processorfrom a non-transitory memory.

In embodiments, the supervised and the unsupervised trained model aretrained in accordance with a neural network machine learning modelhaving a set of input layers interconnected via a set of intermediatelayers to an output layer. The machine learning neural network model isinstantiated in the processor 201 based at least in part uponprocessor-executable instructions being accessed by processor 201 from anon-transitory memory 202.

In one embodiment, training the supervised trained model includesproviding the agglomerated social media content to the input layers ofthe machine learning neural network, the intermediate layers beingconfigured in accordance with an initial matrix of weights. Then,training the machine learning neural network, upon providing of contentpredetermined as being associated with the subject of interest to theoutput layer, based upon recursively adjusting the initial matrix ofweights by backpropogation in diminishment of an error matrix computedat the output layer.

In an embodiment, training the supervised trained model includesproviding content identified as being associated with the subject ofinterest to the set of input layers of the machine learning neuralnetwork, the set of intermediate layers being configured in accordancewith an initial matrix of weights. Then, training the machine learningneural network based at least in part upon recursively adjusting theinitial matrix of weights by backpropogation in diminishment of an errormatrix computed in accordance with a sentiment intensity ratinggenerated at the output layer.

FIG. 7 illustrates an example embodiment of a method 700 of operation ingenerating a transitory sentiment community. Examples of method stepsdescribed herein relate to the use of server 101 for implementing thetechniques described. Method 700 embodiment depicted is performed by oneor more processors 201 of server computing device 101. In describing andperforming the embodiments of FIG. 7, the examples of FIG. 1 throughFIG. 6 are incorporated for purposes of illustrating suitable componentsor elements for performing a step or sub-step being described. Accordingto one embodiment, the techniques are performed by transitory sentimentcommunity logic module 605 of server 601 in response to the processor201 executing one or more sequences of software logic instructions thatconstitute transitory sentiment community logic module 605.

In embodiments, transitory sentiment community logic module 605 caninclude the one or more sequences of instructions within sub-modulesincluding sentiment content identification module 610, sentimentanalysis module 611, and sentiment community generating module 612. Suchinstructions may be read into memory 202 from machine-readable medium,such as memory storage devices. In executing the sequences ofinstructions of sentiment content identification module 610, sentimentanalysis module 611, and sentiment community generating module 612 oftransitory sentiment community logic module 605 in memory 202, processor201 performs the process steps described herein. In alternativeimplementations, at least some hard-wired circuitry may be used in placeof, or in combination with, the software logic instructions to implementexamples described herein. Thus, the examples described herein are notlimited to any particular combination of hardware circuitry and softwareinstructions.

At step 710, in accordance with executing instructions comprisingcontent identification module 610 in processor 201, identifying, inaccordance with a supervised trained model, within agglomerated socialmedia content data, content associated with a subject of interest andcharacterized in accordance with one of a sentiment expressive usage andnot a sentiment expressive usage, the subject of interest defined inaccordance with at least one text character string.

In embodiments the agglomerated social media content includes anycombination of a hashtag, a twitter handle, an emoticon, at least aportion of a website content, a product brand name, a company name, aproduct feature, a message exchange, an image, a video portion, or atext string produced via a speech to text conversion of at least aportion of an audio file source. In other variations, content can beagglomerated based on sources across more than social media content,including news articles and other website content.

At step 720, in accordance with executing instructions of sentimentanalysis module 611 in processor 201, performing, based on anunsupervised trained model in conjunction with content associated withthe sentiment expressive usage, a sentiment analysis that determines asentiment intensity rating associated with at least a portion of theagglomerated social media content data.

At step 730, in accordance with executing instructions comprisingsentiment community generating module 612 in processor 201, generatingthe transitory sentiment community based at least in part on thesentiment intensity rating.

In another embodiment, generating the transitory sentiment community canbe further based on the subject of interest as embedded into theagglomerated social media content data.

In yet another variation, generating the transitory sentiment communitycan be based on either amplifying or attenuating the sentiment intensityrating. For instance, and without limitation to illustrative instances,generating the transitory sentiment community based on amplifying orattenuating the sentiment intensity rating based a personality traitassociated with an author of the content associated with the sentimentexpressive usage, or upon detecting existence of a slang expression inthe content associated with the sentiment expressive usage.

In some embodiments, generating the transitory sentiment community isbased on applying a sentiment intensity threshold to the sentimentintensity rating, the sentiment intensity rating being either above orbelow the sentiment intensity threshold. Some aspects can allow fordynamically adjusting the sentiment intensity threshold, as thesentiment community experiences progressions from one sentiment beingexperienced, in accordance with the determined sentiment ratings, asbeing predominant followed by transition to another different sentimentbeing predominant. In other aspects, determinations of sentimentintensity ratings can be determined and established in accordance withpredetermined time periods or timings.

Other embodiments can include generating the transitory sentimentcommunity based on many sentiment classifications, each sentimentclassification having a respective sentiment intensity threshold, andmodifying or combining at least one sentiment classification with one ormore other sentiment classifications recognized by the unsupervisedtraining model.

It is contemplated that, in regard to illustrative examples herein,where a machine learning method, or training method, is described asbeing based on an unsupervised learning technique, a supervised or asemi-supervised technique may alternately be applied; and where amachine learning method is described as based on a supervised learningtechnique, an unsupervised or partly unsupervised learning technique mayalternately be used.

It is contemplated that embodiments described herein extend toindividual elements and concepts described herein, independently ofother concepts, ideas or system, as well as for embodiments to includecombinations of elements recited anywhere in this application. Althoughembodiments are described in detail herein with reference to theaccompanying drawings, it is to be understood that the invention is notlimited to those precise embodiments. As such, many modifications andvariations will be apparent to practitioners skilled in this art. Forexample, in illustrative examples where a machine learning method isdescribed as being based on an unsupervised learning technique, asupervised learning technique may alternately be applied. And where amachine learning method is described as based on an unsupervisedlearning technique, a supervised learning technique may alternately beapplied. Accordingly, it is intended that the scope of the invention bedefined by the following claims and their equivalents. Furthermore, itis contemplated that a particular feature described either individuallyor as part of an embodiment can be combined with other individuallydescribed features, or parts of other embodiments as described herein.Thus, the absence of describing combinations should not preclude theinventors from claiming rights to such combinations.

What is claimed is:
 1. A method, performed in a processor of a servercomputing device, of generating a transitory sentiment community, themethod comprising: identifying, in accordance with a supervised trainedmodel, within agglomerated social media content data, content associatedwith a subject of interest and characterized in accordance with one of asentiment expressive usage and not a sentiment expressive usage, thesubject of interest defined in accordance with at least one textcharacter string; performing, based on an unsupervised trained model inconjunction with content associated with the sentiment expressive usage,a sentiment analysis that determines a sentiment intensity ratingassociated with at least a portion of the agglomerated social mediacontent data; and generating the transitory sentiment community based atleast in part on the sentiment intensity rating.
 2. The method of claim1 wherein the agglomerated social media content comprises one or moreof: a hashtag, a twitter handle, an emoticon, at least a portion of awebsite content, a product brand name, a product feature, a messageexchange, an image, a video portion, and a text string produced via aspeech to text conversion of at least a portion of an audio file source.3. The method of claim 1 further comprising generating the transitorysentiment community based on the subject of interest as embedded intothe agglomerated social media content data.
 4. The method of claim 1further comprising generating the transitory sentiment community basedon at least one of amplifying and attenuating the sentiment intensityrating.
 5. The method of claim 4 further comprising generating thetransitory sentiment community based on at least one of amplifying andattenuating the sentiment intensity rating based in part on at least oneof: (i) a personality trait associated with an author of the contentassociated with the sentiment expressive usage, and (ii) upon detectingexistence of a slang expression in the content associated with thesentiment expressive usage.
 6. The method of claim 1 wherein thegenerating is based on applying a sentiment intensity threshold to thesentiment intensity rating, the sentiment intensity rating being one ofabove and below the sentiment intensity threshold.
 7. The method ofclaim 1 further comprising: generating the transitory sentimentcommunity based on a set of sentiment classifications, respective onesof the set of sentiment classifications being associated with arespective sentiment intensity threshold; and modifying at least one ofthe set of sentiment classifications to one or more alternate sentimentclassifications recognized by the unsupervised training model.
 8. Themethod of claim 1 wherein at least one of the supervised and theunsupervised trained model is trained, in accordance with a neuralnetwork machine learning model comprising a set of input layersinterconnected via a set of intermediate layers to an output layer, themachine learning neural network model being instantiated in theprocessor based at least in part upon processor-executable instructionsbeing accessed by the processor from a non-transitory memory.
 9. Themethod of claim 8, wherein training the supervised trained modelcomprises: providing the agglomerated social media content to the set ofinput layers of the machine learning neural network, the set ofintermediate layers being configured in accordance with an initialmatrix of weights; and training the machine learning neural network,upon providing of content predetermined as being associated with thesubject of interest to the output layer, based at least in part uponrecursively adjusting the initial matrix of weights by backpropogationin diminishment of an error matrix computed at the output layer.
 10. Themethod of claim 8, wherein training the unsupervised trained modelcomprises: providing content identified as being associated with thesubject of interest to the set of input layers of the machine learningneural network, the set of intermediate layers being configured inaccordance with an initial matrix of weights; and training the machinelearning neural network based at least in part upon recursivelyadjusting the initial matrix of weights by backpropogation indiminishment of an error matrix computed in accordance with a sentimentintensity rating generated at the output layer.
 11. A server computingsystem for generating a transitory sentiment community, the servercomputing system comprising: a processor; a memory storing a set ofinstructions, the instructions when executed in the processor causingoperations comprising: identifying, in accordance with a supervisedtrained model, within agglomerated social media content data, contentassociated with a subject of interest and characterized in accordancewith one of a sentiment expressive usage and not a sentiment expressiveusage, the subject of interest defined in accordance with at least onetext character string; performing, based on an unsupervised trainedmodel in conjunction with content associated with the sentimentexpressive usage, a sentiment analysis that determines a sentimentintensity rating associated with at least a portion of the agglomeratedsocial media content data; and generating the transitory sentimentcommunity based at least in part on the sentiment intensity rating. 12.The server computing system of claim 11 wherein the agglomerated socialmedia content comprises one or more of: a hashtag, a twitter handle, anemoticon, at least a portion of a website content, a product brand name,a product feature, a message exchange, an image, a video portion, and atext string produced via a speech to text conversion of at least aportion of an audio file source.
 13. The server computing system ofclaim 11 further comprising executable instructions causing operationscomprising generating the transitory sentiment community based on thesubject of interest as embedded into the agglomerated social mediacontent data.
 14. The server computing system of claim 11 furthercomprising executable instructions causing operations comprisinggenerating the transitory sentiment community based on at least one ofamplifying and attenuating the sentiment intensity rating.
 15. Theserver computing system of claim 14 further comprising executableinstructions causing operations comprising generating the transitorysentiment community based on at least one of amplifying and attenuatingthe sentiment intensity rating based in part on at least one of: (i) apersonality trait associated with an author of the content associatedwith the sentiment expressive usage, and (ii) upon detecting existenceof a slang expression in the content associated with the sentimentexpressive usage.
 16. The server computing system of claim 11 whereinthe generating is based on applying a sentiment intensity threshold tothe sentiment intensity rating, the sentiment intensity rating being oneof above and below the sentiment intensity threshold.
 17. The servercomputing system of claim 11 further comprising executable instructionscausing operations comprising: generating the transitory sentimentcommunity based on a set of sentiment classifications, respective onesof the set of sentiment classifications being associated with arespective sentiment intensity threshold; and modifying at least one ofthe set of sentiment classifications to one or more alternate sentimentclassifications recognized by the unsupervised training model.
 18. Theserver computing system of claim 11 wherein at least one of thesupervised and the unsupervised trained model is trained, in accordancewith a neural network machine learning model comprising a set of inputlayers interconnected via a set of intermediate layers to an outputlayer, the machine learning neural network model being instantiated inthe processor based at least in part upon processor-executableinstructions being accessed by the processor from a non-transitorymemory.
 19. The server computing system of claim 18, wherein trainingthe supervised trained model comprises: providing the agglomeratedsocial media content to the set of input layers of the machine learningneural network, the set of intermediate layers being configured inaccordance with an initial matrix of weights; and training the machinelearning neural network, upon providing of content predetermined asbeing associated with the subject of interest to the output layer, basedat least in part upon recursively adjusting the initial matrix ofweights by backpropogation in diminishment of an error matrix computedat the output layer.
 20. The server computing system of claim 18,wherein training the supervised trained model comprises: providingcontent identified as being associated with the subject of interest tothe set of input layers of the machine learning neural network, the setof intermediate layers being configured in accordance with an initialmatrix of weights; and training the machine learning neural networkbased at least in part upon recursively adjusting the initial matrix ofweights by backpropogation in diminishment of an error matrix computedin accordance with a sentiment intensity rating generated at the outputlayer.